DATA REDUCTION VERSUS FEATURE SELECTION IN APPLICATION OF DAILY MAXIMUM POWER LOAD FORECASTING
Abstract
Load forecasting task of small energetic region is a difficult problem due to high variability of power consumption. The accurate forecast of the power in the next hours is very important from the economic point of view. The most important problems in prediction are the choice of predictor and selection of features. Two methods of features selection was presented – simple selection using of genetic algorithm and dimensionality reduction methods for creating new features from many available measured data. As a predictor the Support Vector Machine working in regression mode (SVR) was chosen. The load forecasting results with SVR are presented and discussed.
Keywords:
load power forecasting, dimensionality reduction, genetic algorithm, support vector machineReferences
Ashlock D.: Evolutionary Computation for Modeling and Optimization. Berlin, Germany: Springer-Verlag, 2006.
Google Scholar
Fodor I.: A Survey of Dimension Reduction Techniques. Raport techniczny, 2002.
DOI: https://doi.org/10.2172/15002155
Google Scholar
Gill P., Murray W., Wright M.: Practical optimization. Academic Press, London 1981.
Google Scholar
Goldberg D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
Google Scholar
Jackson J.E.: User guide to principal components. Wiley, NY, 1991.
DOI: https://doi.org/10.1002/0471725331
Google Scholar
Osowski S., Siwek K., Świderski B., Mycka Ł.: Prediction of power consumption for small power region using indexing approach and neural network. Lecture Notes on Computer Science, LNCS-6352, 2010, str. 54-59.
DOI: https://doi.org/10.1007/978-3-642-15819-3_8
Google Scholar
Osowski S., Siwek K.: Regularization of neural networks for load forecasting in power system. IEE Proc. GTD, 149, 2002, 340-345.
DOI: https://doi.org/10.1049/ip-gtd:20020194
Google Scholar
Sammon J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, No. 18, 1969, str. 401–409.
DOI: https://doi.org/10.1109/T-C.1969.222678
Google Scholar
Siwek K., Osowski S., Świderski B.: Trend elimination of time series of 24-hour load demand in the power system and its application in power forecasting. Przegląd Elektrotechniczny, vol. 87, No 3, 2011, str. 249-253.
Google Scholar
Schölkopf B., Smola A.: Learning with kernels. MIT Press, Cambridge MA, 2002.
Google Scholar
Vapnik V.: Statistical learning theory. Wiley, NY, 1998.
Google Scholar
Van der Maaten L., Hinton G.: Visualizing Data using t-SNE, Journal of Machine Learning Research, Vol. 9, 2008, str. 2579-2605.
Google Scholar
Van der Maaten L., Postma, E.: Dimensionality reduction: a comparative review. 2009, int. report TiCC TR 2009-005.
Google Scholar
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